18279136. MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Organization Name
Inventor(s)
MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18279136 titled 'MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Simplified Explanation
The model training apparatus described in the patent application trains an image conversion model to generate an output image representing a scene in a second environment from an input image representing a scene in a first environment. The training process involves inputting a training image to the image conversion model to obtain feature maps and output images, computing a patch-wise loss using features from positive and negative example patches, and training the model based on this loss.
- Training an image conversion model to generate output images representing scenes in different environments
- Inputting training images to obtain feature maps and output images
- Computing patch-wise loss using features from example patches
- Training the model based on the computed loss
Potential Applications
This technology could be applied in various fields such as:
- Image editing and enhancement
- Virtual reality and augmented reality
- Environmental simulation and design
Problems Solved
This technology helps in:
- Converting images from one environment to another seamlessly
- Enhancing the quality and realism of image conversion
- Improving the training process for image conversion models
Benefits
The benefits of this technology include:
- Efficient training of image conversion models
- Enhanced image conversion quality
- Versatile applications in different industries
Potential Commercial Applications
Potential commercial applications of this technology include:
- Software development for image editing and conversion
- Integration into virtual reality and augmented reality systems
- Providing services for environmental simulation and design
Possible Prior Art
One possible prior art in this field is the use of Generative Adversarial Networks (GANs) for image-to-image translation tasks.GANs have been used for similar purposes in the past, but the specific method described in this patent application may offer unique advantages or improvements.
Unanswered Questions
How does this technology compare to existing image conversion methods?
This article does not provide a direct comparison with other image conversion methods currently available in the market. It would be interesting to know the specific advantages or differences this technology offers compared to existing solutions.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology in practical scenarios. Understanding these factors could help in assessing the feasibility and scalability of the innovation.
Original Abstract Submitted
The model training apparatus trains an image conversion model to generate, from an input image representing a scene in a first environment, an output image representing the scene in a second environment. The model training apparatus inputs a training image to the image conversion model to obtain a first feature map and an output image, input the output image to the image conversion model to obtain a second feature map, computes a patch-wise loss using the features corresponding to a positive example patch and a negative example patch extracted from the training image and a positive example patch extracted from the output image, and trains the image conversion model based on the patch-wise loss, which is extracted intensively from the region representing an object of a specific type.
